mirror of
https://github.com/ROCm/composable_kernel.git
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79b0bfeb41
* Add gemm_mx_fp8_bf8 example with row-major B * Add more overloads of MX MFMA instructions * Add MK_KN (RRR) tests * Add KM_NK (CCR) tests * Add more problem sizes to Large tests * Add test_gemm_mx to the list of regression tests
499 lines
20 KiB
C++
499 lines
20 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <gtest/gtest.h>
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#include "ck/utility/data_type.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/utility/number.hpp"
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#include "ck/library/utility/literals.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/fill.hpp"
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#include "ck/tensor_operation/gpu/device/device_gemm_mx.hpp"
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#include "ck/library/tensor_operation_instance/gpu/gemm_mx.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_mx_gemm.hpp"
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#include "ck/library/utility/check_err.hpp"
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namespace ck {
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namespace test {
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namespace {
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using Row = ck::tensor_layout::gemm::RowMajor;
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using Col = ck::tensor_layout::gemm::ColumnMajor;
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} // namespace
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template <typename ADataType,
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typename BDataType,
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typename CDataType,
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typename ALayout,
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typename BLayout,
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typename CLayout,
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int ScaleBlockSize>
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bool profile_gemm_mx_impl(int do_verification,
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int init_method,
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bool do_log,
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bool time_kernel,
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int M,
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int N,
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int K,
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int StrideA,
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int StrideB,
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int StrideC,
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int KBatch,
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int n_warmup,
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int n_iter,
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uint64_t rotating = 0)
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{
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if(K % ScaleBlockSize != 0)
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{
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throw std::runtime_error("wrong! K must be multiple of ScaleBlockSize.");
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};
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using ScaleDataType = e8m0_bexp_t;
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using AScaleLayout = Row;
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using BScaleLayout = Col;
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bool pass = true;
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auto f_host_tensor_descriptor =
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[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
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using namespace ck::literals;
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if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
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{
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return HostTensorDescriptor({row, col}, {stride, 1_uz});
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}
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else
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{
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return HostTensorDescriptor({row, col}, {1_uz, stride});
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}
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};
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auto f_get_default_stride =
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[](ck::index_t row, ck::index_t col, ck::index_t stride, auto layout) {
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if(stride == -1)
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{
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// give a chance if stride is -1, return a default packed stride
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if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
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{
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return static_cast<ck::index_t>(col);
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}
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else
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{
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return static_cast<ck::index_t>(row);
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}
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}
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else
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return static_cast<ck::index_t>(stride);
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};
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auto Scale_Stride_AM = f_get_default_stride(M, K / ScaleBlockSize, -1, AScaleLayout{});
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auto Scale_Stride_BN = f_get_default_stride(K / ScaleBlockSize, N, -1, BScaleLayout{});
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Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
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Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
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Tensor<ScaleDataType> a_m_k_scale(f_host_tensor_descriptor(
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M, K / ScaleBlockSize, Scale_Stride_AM, AScaleLayout{})); // scales for A
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Tensor<ScaleDataType> b_k_n_scale(f_host_tensor_descriptor(
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K / ScaleBlockSize, N, Scale_Stride_BN, BScaleLayout{})); // scales for B
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Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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std::size_t total_gemm_needed =
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a_m_k.GetElementSpaceSizeInBytes() + b_k_n.GetElementSpaceSizeInBytes() +
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a_m_k_scale.GetElementSpaceSizeInBytes() + b_k_n_scale.GetElementSpaceSizeInBytes();
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int rotating_count = std::max(
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1,
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std::min(n_iter,
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static_cast<int>(std::ceil(static_cast<double>(rotating) / total_gemm_needed))));
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std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
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std::cout << "a_m_k_scale: " << a_m_k_scale.mDesc << std::endl;
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std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
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std::cout << "b_k_n_scale: " << b_k_n_scale.mDesc << std::endl;
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std::cout << "c_m_n: " << c_m_n_device_result.mDesc << std::endl;
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std::cout << "rotating count: " << rotating_count << std::endl;
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switch(init_method)
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{
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case 0: // Initializations for development and debugging
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ck::utils::FillConstant<ADataType>{ck::type_convert<ADataType>(1.0f)}(a_m_k);
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ck::utils::FillConstant<ScaleDataType>{ck::type_convert<ScaleDataType>(2.0f)}(a_m_k_scale);
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ck::utils::FillConstant<BDataType>{ck::type_convert<BDataType>(0.5f)}(b_k_n);
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ck::utils::FillConstant<ScaleDataType>{ck::type_convert<ScaleDataType>(1.0f)}(b_k_n_scale);
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if(do_log)
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{
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std::cout << "Init A = {1}" << std::endl;
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std::cout << "Init A scale = {2.0}" << std::endl;
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std::cout << "Init B = {0.5}" << std::endl;
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std::cout << "Init B scale = {1.0}" << std::endl;
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std::cout << "Expect C = {K}" << std::endl;
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}
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break;
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case 1:
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a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-4, 5}); // Z[-4,4]
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b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-4, 5}); // Z[-4,4]
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a_m_k_scale.GenerateTensorValue(
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GeneratorTensor_2<ScaleDataType>{125, 129}); // scales: {0.25, 0.5, 1, 2}
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b_k_n_scale.GenerateTensorValue(
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GeneratorTensor_2<ScaleDataType>{125, 129}); // scales: {0.25, 0.5, 1, 2}
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break;
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default:
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a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{-2.0, 2.0});
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a_m_k_scale.GenerateTensorValue(
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GeneratorTensor_3<ScaleDataType>{powf(2.0f, -125.0f), 1.0f}); // R[2^-125, 1]
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b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-2.0, 2.0});
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b_k_n_scale.GenerateTensorValue(
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GeneratorTensor_3<ScaleDataType>{powf(2.0f, -125.0f), 1.0f});
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break;
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}
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using AElementOp = ck::tensor_operation::element_wise::PassThrough;
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using BElementOp = ck::tensor_operation::element_wise::PassThrough;
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using CElementOp = ck::tensor_operation::element_wise::PassThrough;
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const auto a_element_op = AElementOp{};
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const auto b_element_op = BElementOp{};
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const auto c_element_op = CElementOp{};
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if(do_log > 0)
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std::cout << "Device memory allocation..." << std::endl;
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DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
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DeviceMem a_scale_device_buf(sizeof(ScaleDataType) * a_m_k_scale.mDesc.GetElementSpaceSize());
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DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
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DeviceMem b_scale_device_buf(sizeof(ScaleDataType) * b_k_n_scale.mDesc.GetElementSpaceSize());
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DeviceMem c_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
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if(do_log > 0)
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std::cout << "Upload data to device..." << std::endl;
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a_device_buf.ToDevice(a_m_k.mData.data());
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a_scale_device_buf.ToDevice(a_m_k_scale.mData.data());
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b_device_buf.ToDevice(b_k_n.mData.data());
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b_scale_device_buf.ToDevice(b_k_n_scale.mData.data());
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if(do_log > 0)
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std::cout << "Done." << std::endl;
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using DeviceOp = ck::tensor_operation::device::DeviceGemmMX<ALayout,
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BLayout,
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CLayout,
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ADataType,
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ScaleDataType,
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BDataType,
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ScaleDataType,
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CDataType,
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ScaleBlockSize,
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AElementOp,
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BElementOp,
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CElementOp>;
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// get device op instances
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const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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DeviceOp>::GetInstances();
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std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
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// Run reference GEMM
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if(do_verification)
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{
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using ReferenceGemmInstance =
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ck::tensor_operation::host::ReferenceMXGemm<ADataType,
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BDataType,
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CDataType,
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float, // AccDataType
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ScaleDataType,
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AElementOp,
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BElementOp,
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CElementOp,
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float, // ComputeTypeA
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float // ComputeTypeB
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>;
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auto ref_gemm = ReferenceGemmInstance{};
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auto ref_invoker = ref_gemm.MakeInvoker();
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auto ref_argument = ref_gemm.MakeArgument(a_m_k,
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a_m_k_scale,
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b_k_n,
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b_k_n_scale,
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c_m_n_host_result,
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a_element_op,
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b_element_op,
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c_element_op);
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ref_invoker.Run(ref_argument);
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}
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std::string best_op_name;
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std::optional<std::string> best_op_object_name;
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float best_ave_time = 0;
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float best_tflops = 0;
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float best_gb_per_sec = 0;
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float best_kbatch = 0;
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// profile device GEMM instances
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for(auto& op_ptr : op_ptrs)
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{
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std::vector<int> kbatch_list = {1, 2, 4, 8, 16, 19, 32, 38}; // use these when KBatch <= 0
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if(KBatch > 0)
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{
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kbatch_list = {KBatch};
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}
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for(std::size_t i = 0; i < kbatch_list.size(); i++)
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{
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auto kbatch_curr = kbatch_list[i];
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auto argument_ptr = op_ptr->MakeArgumentPointer(
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static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
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static_cast<ScaleDataType*>(a_scale_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
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static_cast<ScaleDataType*>(b_scale_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
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M,
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N,
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K,
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StrideA,
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Scale_Stride_AM,
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StrideB,
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Scale_Stride_BN,
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StrideC,
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kbatch_curr,
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a_element_op,
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b_element_op,
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c_element_op);
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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// re-init C to zero before profiling next kernel
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c_device_buf.SetZero();
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invoker_ptr->Run(argument_ptr.get(),
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StreamConfig{nullptr, false, 0, n_warmup, n_iter});
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if(do_verification)
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{
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c_device_buf.FromDevice(c_m_n_device_result.mData.data());
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if(do_log)
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{
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if(init_method == 0)
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{
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auto expected = static_cast<float>(K);
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auto computed = type_convert<float>(c_m_n_device_result(0, 12));
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pass = pass & (std::abs(expected - computed) <= 0.0f);
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std::cout << "\nExpected vs Computed: " << expected << " vs "
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<< computed << ((pass) ? " (PASSED!)" : " (FAILED!)")
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<< std::endl
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<< std::endl;
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}
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else
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{
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LogRangeAsType<float>(std::cout << "a : ", a_m_k.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(std::cout << "a_scale : ", a_m_k_scale.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(std::cout << "b: ", b_k_n.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(std::cout << "b_scale: ", b_k_n_scale.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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std::cout << "c_host : ", c_m_n_host_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(
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std::cout << "c_device: ", c_m_n_device_result.mData, ",")
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<< std::endl;
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}
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}
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pass = pass & ck::utils::check_err(c_m_n_device_result, c_m_n_host_result);
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}
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std::string op_name = op_ptr->GetTypeString();
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std::optional<std::string> op_obj_name = op_ptr->GetObjectName();
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float ave_time = invoker_ptr->Run(argument_ptr.get(),
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StreamConfig{nullptr,
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time_kernel,
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0,
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n_warmup,
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n_iter,
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rotating_count > 1,
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rotating_count});
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// Output size(M*N) * [dot product(2K) + product of scales(K/ScaleBlockSize) +
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// scaling of partial sums(K/ScaleBlockSize)]
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// FLOPS = 2 * M * N * K + 2 * M * N * K / ScaleBlockSize
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std::size_t flop =
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std::size_t(2) * M * N * K + std::size_t(2) * M * N * K / ScaleBlockSize;
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std::size_t num_btype = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
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sizeof(CDataType) * M * N +
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sizeof(ScaleDataType) * (M * K + K * N) / ScaleBlockSize;
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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float gb_per_sec = num_btype / 1.E6 / ave_time;
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std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops
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<< " TFlops, " << gb_per_sec << " GB/s, " << op_name << ", KBatch "
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<< kbatch_curr << std::endl;
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if(tflops > best_tflops && ave_time > 1e-10)
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{
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best_op_name = op_name;
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best_op_object_name = op_obj_name;
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best_tflops = tflops;
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best_ave_time = ave_time;
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best_gb_per_sec = gb_per_sec;
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best_kbatch = kbatch_curr;
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}
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}
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else
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{
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std::cout << op_ptr->GetTypeString() << " does not support this problem"
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<< std::endl;
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}
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}
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}
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if constexpr(is_same<CDataType, float>::value)
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{
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std::cout << "Best Perf for datatype = f32";
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}
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else if constexpr(is_same<CDataType, half_t>::value)
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{
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std::cout << "Best Perf for datatype = f16";
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}
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else if constexpr(is_same<CDataType, bhalf_t>::value)
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{
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std::cout << "Best Perf for datatype = bf16";
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}
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else if constexpr(is_same<CDataType, int8_t>::value)
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{
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std::cout << "Best Perf for datatype = int8";
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}
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if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value)
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{
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std::cout << " ALayout = RowMajor";
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}
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else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value)
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{
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std::cout << " ALayout = ColumnMajor";
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}
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if constexpr(is_same<BLayout, tensor_layout::gemm::RowMajor>::value)
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{
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std::cout << " BLayout = RowMajor";
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}
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else if constexpr(is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value)
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{
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std::cout << " BLayout = ColumnMajor";
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}
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std::cout << " M = " << M << " N = " << N << " K = " << K << " StrideA = " << StrideA
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<< " StrideB = " << StrideB << " StrideC = " << StrideC << " KBatch = " << best_kbatch
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<< " : " << best_ave_time << " ms, " << best_tflops << " TFlops, " << best_gb_per_sec
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<< " GB/s, " << best_op_name << std::endl;
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if(best_op_object_name)
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std::cout << best_op_object_name.value() << std::endl;
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return pass;
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}
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template <typename Tuple>
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class TestGemmMX : public testing::Test
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{
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using Row = ck::tensor_layout::gemm::RowMajor;
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using F32 = float;
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using ScaleType = e8m0_bexp_t;
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protected:
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using ALayout = std::tuple_element_t<0, Tuple>;
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using BLayout = std::tuple_element_t<1, Tuple>;
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using CLayout = Row;
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using ADataType = std::tuple_element_t<2, Tuple>;
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using BDataType = std::tuple_element_t<3, Tuple>;
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using CDataType = std::tuple_element_t<4, Tuple>;
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using AccDataType = float;
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public:
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static constexpr index_t ScaleBlockSize = std::tuple_element_t<5, Tuple>{};
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static constexpr bool verify_ = true;
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static constexpr int init_method_ = 2; // decimal value initialization
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static constexpr bool log_ = false;
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static constexpr bool bench_ = false; // measure kernel performance
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std::vector<int> k_batches_;
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void SetUp() override { k_batches_ = {1}; }
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void Run(const int M,
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const int N,
|
|
const int K,
|
|
const int StrideA,
|
|
const int StrideB,
|
|
const int StrideC)
|
|
{
|
|
for(auto kb : k_batches_)
|
|
{
|
|
RunSingle(M, N, K, StrideA, StrideB, StrideC, kb);
|
|
}
|
|
}
|
|
|
|
void RunSingle(const int M,
|
|
const int N,
|
|
const int K,
|
|
const int StrideA,
|
|
const int StrideB,
|
|
const int StrideC,
|
|
int kbatch = 1,
|
|
int n_warmup = 1,
|
|
int n_iter = 10)
|
|
{
|
|
bool pass = ck::test::profile_gemm_mx_impl<ADataType,
|
|
BDataType,
|
|
CDataType,
|
|
ALayout,
|
|
BLayout,
|
|
CLayout,
|
|
ScaleBlockSize>(verify_,
|
|
init_method_,
|
|
log_,
|
|
bench_,
|
|
M,
|
|
N,
|
|
K,
|
|
StrideA,
|
|
StrideB,
|
|
StrideC,
|
|
kbatch,
|
|
n_warmup,
|
|
n_iter);
|
|
EXPECT_TRUE(pass);
|
|
}
|
|
};
|
|
|
|
} // namespace test
|
|
} // namespace ck
|